inkGround is a new benchmark for evaluating how well Vision‑Language Models can recover the fine‑grained stroke trajectories of cursive handwriting from camera‑captured page images. It contains 537 full‑page images with 18.7k words and 163k strokes, paired with pseudo‑trajectory references generated by an offline‑to‑online model and verified by humans. The benchmark casts the task as text‑conditioned point grounding, scoring models on trajectory fidelity, readability, and style similarity, and compares both open‑source and closed‑source VLMs, showing that closed‑source models like Gemini‑3.1‑Pro perform best.
By Sidra Hanif
arXiv:2607. 26736v1 Announce Type: new Abstract: Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation.
By Florent Imbert, Romain Tavenard, Yann Soullard, Eric Anquetil
arXiv:2607. 26708v1 Announce Type: new Abstract: The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction.
By Florent Imbert, Eric Anquetil, Yann Soullard, Romain Tavenard
Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped with kinematic sensors, allowing users to write on any surface while simultaneously preserving a digital trajectory of handwriting.
arXiv:2609.37195v1 Announce Type: new
Abstract: Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents. Not only do they cut down ti...
By Eric Ayllon, Abel Gandia, Jorge Calvo-Zaragoza
arXiv:2607. 26733v1 Announce Type: cross Abstract: Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction.
By Wassim Swaileh, Florent Imbert, Yann Soullard, Romain Tavenard, Eric Anquetil
Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings.
The paper presents a two‑stage method for recovering handwriting trajectories from static images. First, it predicts ordered stroke instances autoregressively, then reconstructs continuous motion within each stroke using direction‑related cues. Experiments on Chinese, English, and Tamil handwriting show that this ordered prediction outperforms post‑hoc ordering and baseline models, and that sampling density significantly impacts performance.
By En-Guang Wang, Yan-Ming Zhang, Fei Yin, Cheng-Lin Liu
OmniHandwritingOCR is a diagnostic benchmark designed to evaluate multimodal large language models (MLLMs) and OCR systems on handwritten text and mathematical expression recognition. It comprises 77.57K labeled images across six subtasks and twelve subsets, including a difficulty‑stratified multi‑line formula corpus that tests robustness to increasing structural complexity. The benchmark reveals that current systems perform poorly on complex multi‑line formulas, exhibit variable rankings across languages and formula settings, and sometimes hallucinate corrections that are not visually supported.
By Zinuo Guo, Min Zhang, Bo Jiang
arXiv:2609.17565v1 Announce Type: new
Abstract: Online handwriting recognition systems typically represent pen trajectories through fixed-length Euclidean shape descriptors that capture the spatial o...
By Hassan Ugail, Newton Howard
arXiv:2604.02103v3 Announce Type: replace-cross
Abstract: Realistic online handwriting depends not only on individual character shapes, but also on how a writer connects, spaces, and aligns adjacent...
By Jinsu Shin, Sungeun Hong, JinYeong Bak
The paper explores how synthetic data can be used to train a Thai OCR model without real OCR labels. By systematically varying factors such as typeface diversity, page structure, and handwriting glyphs, the authors identify which aspects of realism most improve transfer to real documents. Using these insights, they adapt a large PaddleOCR model into Wayu‑Paxa‑OCR‑Zero, achieving a median character error rate of 1.24% on printed pages and 20.55% on handwriting, outperforming existing Thai OCR systems.
By Kunat Pipatanakul